For both algorithms we use smoothed finite difference differentiation with a smoothing window length of 21 to accommodate the noisier data. As before, for SR3 we tune over \(\nu \in \lbrace \frac{1}{30}, \frac{1}{10}, \frac{1}{3}, 1, \frac{10}{3} \rbrace \)  and 50 thresholds \(\lambda =10^a, \ a \in [-3: 0: 20]\) . For MIOSR, we tune over a global sparsity constraint \(k \in [2, 10]\) , and regularizer in \(\lbrace 0.0001, 0.001, 0.01\rbrace \) . As in [1]}, we sample initial conditions uniformly for each dimension in \([-\pi , \pi ]\) .
